It's now a hired gun for thousands of companies in at least 20 industries. David Kenny took the helm of IBM's Watson Group ibm in February, after Big Blue acquired The Weather Company, where Kenny had served as CEO. In the months since then, the Watson business has grown dramatically, with well over 100,000 developers worldwide now working with more than three dozen Watson application program interfaces (APIs). Fortune Deputy Editor Clifton Leaf caught up with Kenny in mid-October, when IBM Watson's General Manager was in San Francisco, getting ready to open Watson West--the AI system's newest business outpost--and to launch the company's second World of Watson conference, a gathering of its burgeoning ecosystem of partners and users, in Las Vegas on Oct. 24. KENNY: Deep learning is a subset of machine learning, which essentially is a set of algorithms. Deep-learning uses more advanced things like convolutional neural networks, which basically means you can look at things more deeply into more layers. Machine learning could work, for example, when it came to reading text.

Our analytics engine Kairos processes unstructured data from millions of sites, blogs, and social platforms like Twitter and Tumblr. Billions of public posts are then analyzed and classified across 25,000 topics, emotions, and demographics--turning noisy social data into insights. In order to create predictions around the elections using our analytics platform Kairos, we built 4 metrics: Awareness, Positivity, Negativity and Intent, of which only Negativity and Intent proved to be valuable in predicting elections. Negativity and Intent are natural language processing classifiers which take advantage of sentence structure as well as keyword matching. Then we modeled the data against survey polls, primary results, and survey pools to obtain weights of influence for each of the social indices.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

On November 6 2014, Amazon announced Echo, a smart speaker featuring a voice-controlled assistant service called Alexa. The company didn't exactly go out of its way to whip up excitement for these newcomers. In sharp contrast to the rollout of its ill-fated Fire Phone--which kicked off with a press-conference extravaganza starring Jeff Bezos--it simply disclosed that Echo existed and would be sold, at first, on an invitation-only basis to Prime members. But Echo spoke for itself--and not just literally. People quickly saw that the pint-size cylinder was a genuinely new kind of consumer-electronics device.

Our brains can comprehend things so well that it makes vision seem very easy. It doesn't take any time for a human to detect an anomaly, or identify the difference between a bus and a car, or to detect and recognize a human face, but it is incredibly hard for a computer to learn how to detect and recognize an object as easy as a human brain. In the last couple of years researchers have made tremendous progress on addressing this problem. They have come up with a solution using deep convolutional neural networks, a model which can perform hard visual recognition tasks which are close to or sometimes even better than the human brain. Convolutional Neural Networks, is a black box that constructs features we would otherwise have to handcraft ourselves, hence to create one it takes very high computing power and a lot of time.

Human resources departments rarely, if ever, are thought of as cutting edge when it comes to the use of technology. A closer look, however, shows the implementation of new technologies, including solutions powered by Artificial Intelligence (AI), in almost every aspect of the talent function. According to a recent Towers Watson HR Service Delivery and Technology Survey, HR professionals are overhauling structure to improve quality and efficiency with 33% of the group spending significantly more on technology in the last year. HR's investment in new technology has also spurred the creation of new data sources. Data around employee productivity, wellness, manager effectiveness, and a host of other activities is quickly dwarfing the traditional data set that HR has traditionally been using.

One of the most difficult challenges reporting and analytics face in public relations measurement is sentiment analysis. Machines attempt textual analysis of sentiment all the time; more often than not, it goes horribly wrong. How does it go wrong? Machines are incapable of understanding context. Machines are typically programmed to look for certain keywords as proxies for sentiment.

When Elon Musk isn't outlining plans to use his massive rocket to leave a decaying Planet Earth and colonize Mars, he sometimes talks about his belief that Earth isn't even real and we probably live in a computer simulation. "There's a billion to one chance we're living in base reality," he said at a conference in June. Musk is just one of the people in Silicon Valley to take a keen interest in the "simulation hypothesis", which argues that what we experience as reality is actually a giant computer simulation created by a more sophisticated intelligence. If it sounds a lot like The Matrix, that's because it is. According to this week's New Yorker profile of Y Combinator venture capitalist Sam Altman, there are two tech billionaires secretly engaging scientists to work on breaking us out of the simulation.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

Make it easy for your audience to quickly determine what they're about to digest. Use an abstract or introduction to recall your objectives and clearly state them for your readers. What is the problem that you've set out to solve? If you have a desired outcome or any expectations of your audience, say it, as this is the entire reason you're presenting them with your analysis. You then cover everything from your preamble in this section: the question you've been on a mission to answer, your hypothesis, and the methodology you've used.